Abstract
In order to solve the problem of detecting various types of complex fabric defects such as different scale sizes, high fusion with the background and extreme aspect ratios generated in actual production environment, this paper proposes a defect detection method that combines super-resolution reconstruction technology with object detection technology. Firstly, the dataset is reconstructed using the super-resolution reconstruction technology RDN-LTE, which effectively solve the problem of high fusion between defects and background. Furthermore, the copy-paste technology is employed for data augmentation to enhance model robustness. Then the dataset is fed into the detection network DINO for training. To improve the receptive field of the model, Swim Transformer is used as the backbone network of the model instead of ResNet-50, and the scale features extracted by the model are increased from 4 to 5. The deformable attention mechanism is also introduced in the third and fourth stages of Swim Transformer to enhance the global relationship modeling. Finally, multi-scale training method is introduced to capture the defect features at different scales to further improve the model detection effect and training speed. The results of the three kinds of comparative experiments show that the method based on RDN-LTE and improved DINO has a better overall recognition rate for multiple kinds of fabric defects than other current methods.
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Yao, L., Chen, Z., Wan, Y. (2024). Research on Fabric Defect Detection Technology Based on RDN-LTE and Improved DINO. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_11
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